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mBART

mBART is a machine learning tool that uses a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. This means that it can learn from a variety of different languages to help with translation. The input texts are noised by masking phrases and permuting sentences, and a single Transformer model is learned to recover the texts. What is mBART? mBART is a machine learning tool that helps with translation by using larg

mBARTHez

If you're interested in natural language processing and machine learning, you might have heard of mBARTHez. This is a language model that uses transfer learning to improve the French language processing abilities of computers. mBARTHez is unique in that both its encoder and decoder are pre-trained, making it an excellent choice for generative tasks. What is Transfer Learning? Transfer learning is a technique that allows models to learn from one task and apply that knowledge to a related task

mBERT

mBERT, or Multilingual Bidirectional Encoder Representations from Transformers, is a powerful language model developed by Google that can understand and interpret text across 104 languages. This cutting-edge natural language processing technology is considered a major milestone in the field of multilingual computer-based translation and has opened up new possibilities in sectors such as machine learning, artificial intelligence, and big data. In this article, we'll explore the key features and c

MCKERNEL

Overview of McKernel: A Framework for Kernel Approximates in the Mini-Batch Setting McKernel is a framework introduced to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. This core library was developed in 2014 as an integral part of a thesis at Carnegie Mellon and City University of Hong Kong. The original intention was to implement a speedup of Random Kitchen Sinks by writing a very efficient HADAMARD transform, which

MDETR

MDETR is a cutting-edge technology that has revolutionized the field of computer vision. It is an end-to-end modulated detector that uses a new approach to detect objects in an image by using a raw text query, such as a caption or a question. Transformer-based Architecture The MDETR network uses a transformer-based architecture that allows it to reason jointly over text and images in a single model. This fusion of text and image at an early stage of the model leads to better detection accurac

Mean Shift Clustering

Clustering is a technique that helps us group similar items together. Imagine you have a bag of colorful candies, and you want to organize them by color. You would naturally group the red candies together, the blue candies together, and so on. Clustering algorithms do something similar, but with data points instead of candies. One such algorithm is called "Mean Shift Clustering," and in this article, we'll explore how it works in a simple and intuitive way. Mean shift Mean shift is based o

Mechanism Transfer

Mechanism Transfer: A Solid Statistical Basis for Domain AdaptationMechanism Transfer is a technique for few-shot domain adaptation that uses a meta-distributional scenario in which a data generating mechanism is invariant across different domains. This technique is designed to accommodate nonparametric shifts that may result in different distributions across domains, but provides a statistical basis for domain adaptation. In this article, we will provide an overview of Mechanism Transfer, how i

Medical Code Prediction

Medical professionals often take notes on a patient's diagnosis, treatment, and other medical conditions. These notes, referred to as clinical notes, are long, and consist of medical-specific language that is difficult to understand for individuals outside of healthcare. Enter medical coding, a system of assigning numbers to different medical diagnoses, procedures, and treatments. These codes allow medical professionals to communicate with each other more quickly and accurately, but the process

Medical Diagnosis

Medical Diagnosis: Understanding the Process of Identifying Diseases Medical diagnosis is an essential part of the healthcare system. It is the process of identifying the disease that a patient is affected by, based on various factors such as risk factors, signs, symptoms, and results of exams. The aim of the diagnosis is to determine the cause of the disease or ailment a patient is experiencing to properly provide appropriate treatment. Why is Medical Diagnosis Important? Proper medical dia

Medical Image Segmentation

Medical image segmentation is a type of computer vision task in which an image is divided into various segments based on the objects or structures within it. The main objective of this task is to provide an accurate and precise representation of the objects of interest in the image, typically for diagnosis, treatment planning, and quantitative analysis. What is medical imaging? Medical imaging refers to various techniques and technologies used to create images of parts or functions of the hum

Medical Relation Extraction

Medical Relation Extraction: Understanding Medical Textual Data to Improve Medical Care In the field of medicine, information is vital. Medical professionals must have access to the most accurate information possible to diagnose and treat illnesses effectively. With the amount of research coming out every day, it is essential to find ways to process and understand this information as efficiently as possible. What is Medical Relation Extraction? Medical Relation Extraction is the process of i

Meena

Meet Meena: A Cutting-Edge Chatbot Meena is a chatbot that is making waves in the world of artificial intelligence. This innovative tool is trained to hold multi-turn conversations by mining and filtering publicly available social media conversations. With a 2.6-billion parameter neural network, Meena uses a seq2seq model with the evolved transformer as its main architecture. At its core, Meena is designed to minimize the perplexity of the next token, which essentially means that it is trained

MelGAN Residual Block

Audio generation has long been an area of interest in the field of deep learning. The MelGAN Residual Block is a convolutional residual block used in the MelGAN generative audio architecture, aimed to generate high-quality audio waveforms from mel-spectrogram input at high sampling rates. What is a Residual Block? A residual block is a shortcut connection from input to output, designed to overcome the issues of gradient vanishing or exploding. The residual connections provide an alternative a

MelGAN

MelGAN is an exciting development in audio waveform generation using a GAN setup. It is a fully convolutional feed-forward network that takes a mel-spectrogram as input and outputs raw waveform. What is Mel-spectrogram? A mel-spectrogram represents the frequency content of a signal at different points in time. In other words, it is a visual representation of sound that shows how much energy is present in a particular frequency band at a particular time. The y-axis of a mel-spectrogram represe

Memory-Associated Differential Learning

What is MAD Learning? MAD Learning is a new method of learning that makes use of our brain's ability to remember information in order to make predictions about new information. This is done by inferring from the memorized facts we already know to predict what we want to know. Developed by researchers, MAD Learning is a powerful tool that allows individuals to learn complex information more efficiently than traditional learning methods. How does MAD Learning work? When we learn something new,

Memory Network

Understanding Memory Network: Improving Neural Networks with Extended Memory With the advent of artificial intelligence, neural networks have proved to be extremely useful in various fields such as speech recognition, image classification, and natural language processing. However, most traditional neural networks lack a long-term memory component, which can hinder their performance. Memory Network is a novel architecture that aims to address the limitations of traditional neural networks by usi

Mesh-TensorFlow

Overview of Mesh-TensorFlow Mesh-TensorFlow is a programming language used to distribute tensor computations. Like data-parallelism that splits tensors and operations along the "batch" dimension, Mesh-TensorFlow can split any dimensions of a multi-dimensional mesh of processors. This allows users to specify the exact dimensions to be split across any dimensions of the mesh of processors. What is Tensor Computation? Tensor computation is a concept in which matrices and higher-dimensional arra

MeshGraphNet

Introduction to MeshGraphNet MeshGraphNet is a framework that helps machines learn about a new form of simulations to produce accurate results. This framework comprises graph neural networks that execute message passing on a mesh graph and adapt the mesh discretization during forward simulation. The MeshGraphNet model is taught using one-step supervision and an Encode-Process-Decode architecture. This model can generate long pathways inferences iteratively. The framework's primary focus is to l

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Model Blocks Audio to Text Augmented Reality Methods Auto Parallel Methods Autoencoding Transformers AutoML Autoregressive Transformers Backbone Architectures Bare Metal Bare Metal Cloud Bayesian Reinforcement Learning Behaviour Policies Bidirectional Recurrent Neural Networks Bijective Transformation Binary Neural Networks Board Game Models Bot Detection Cache Replacement Models CAD Design Models Card Game Models Cashier-Free Shopping ChatGPT ChatGPT Courses ChatGPT Plugins ChatGPT Tools Cloud GPU Clustering Code Generation Transformers Computer Code Computer Vision Computer Vision Courses Conditional Image-to-Image Translation Models Confidence Calibration Confidence Estimators Contextualized Word Embeddings Control and Decision Systems Conversational AI Tools Conversational Models Convolutional Neural Networks Convolutions Copy Mechanisms Counting Methods Data Analysis Courses Data Parallel Methods Deep Learning Courses Deep Tabular Learning Degridding Density Ratio Learning 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Inpainting Models Video Instance Segmentation Models Video Interpolation Models Video Model Blocks Video Object Segmentation Models Video Panoptic Segmentation Models Video Recognition Models Video Super-Resolution Models Video-Text Retrieval Models Vision and Language Pre-Trained Models Vision Transformers VQA Models Webpage Object Detection Pipeline Website Monitoring Whitening Word Embeddings Working Memory Models